{"id":"W1986871321","doi":"10.1145/362084.362142","title":"Knowledge discovery in data warehouses","year":2000,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Online analytical processing; Data warehouse; Automatic summarization; Terabyte; Knowledge extraction; Data mining; Process (computing); Data science; Information retrieval; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001854531,0.00009724587,0.0001176418,0.00006199568,0.00006903027,0.0001988157,0.003951747,0.00003634512,0.00006987739],"category_scores_gemma":[0.00007342285,0.00008785834,0.00001925148,0.0004517202,0.0000287486,0.001517393,0.001074791,0.0001110289,0.0004528978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002033822,"about_ca_system_score_gemma":0.00006811039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002167849,"about_ca_topic_score_gemma":0.0003479318,"domain_scores_codex":[0.9989541,0.0000299274,0.0001835024,0.0005243922,0.00009032308,0.0002177993],"domain_scores_gemma":[0.9967424,0.0001632391,0.00002836031,0.003003991,0.00001200757,0.00005006001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001365828,0.00009613569,0.0005452315,0.000002829607,0.000003267977,0.000004084459,0.0001502886,0.000004802955,0.00002107445,0.0009323788,0.01252424,0.9857143],"study_design_scores_gemma":[0.0003884101,0.00005029528,0.008430415,0.0000550423,0.000005541555,0.00001345269,0.00002861219,0.1085044,0.000123444,0.005035246,0.8769978,0.0003673561],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4347016,0.001858088,0.5130658,0.01180466,0.00165667,0.0009276677,0.0005577902,0.001320849,0.0341069],"genre_scores_gemma":[0.3975945,0.0009353328,0.5795273,0.0006750817,0.0005729471,0.0001283862,0.0003384858,0.000051413,0.0201766],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9853469,"threshold_uncertainty_score":0.7343394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06937896728329003,"score_gpt":0.3180131510689414,"score_spread":0.2486341837856514,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}